Last refreshed 2026-10-07. Next refresh: weekly.
Why use Nano Banana 2.1 (Gemini Nano Banana 2.1) on Google AI Studio?
Google AI Studio offers Nano Banana 2.1 (Gemini Nano Banana 2.1) with pay-as-you-go pricing at $1.50/1M input tokens. Google AI Studio is a model prototyping environment and API access point for Gemini models, offering an inference playground for developers to test and build AI applications.
Compare Nano Banana 2.1 (Gemini Nano Banana 2.1) across 3 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-genaiexport GOOGLE_API_KEY=...import os
from google import genai
client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
response = client.models.generate_content(gemini-nano-banana-2.1Request example
import os
from google import genai
client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
response = client.models.generate_content(
model="gemini-nano-banana-2.1",
contents="Hello"
)
print(response.text)Gotchas
- Use the model name directly, e.g. "gemini-2.0-flash", "gemini-1.5-pro", or "gemini-2.5-pro-preview-05-06".
- The examples expect GOOGLE_API_KEY; rename it only if your application config maps the new variable.
Compare Nano Banana 2.1 (Gemini Nano Banana 2.1) Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Google AI Studio | $1.50 | $7.50 |
| OpenRouter | $1.50 | $7.50 |
| Vercel AI Gateway | $1.50 | $7.50 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.50 |
| Output tokens | $7.50 |
| Image input | $1.00 |
| Video input | $1.00 |
Capabilities
About Nano Banana 2.1 (Gemini Nano Banana 2.1)
Nano Banana 2.1 (API id gemini-nano-banana-2.1, GA 6 October 2026) is Google's high-efficiency Gemini image generation and conversational editing model, succeeding Nano Banana 2 (gemini-3.1-flash-image). First-party Gemini API docs: text/image/video/PDF in, image+text out; 131,072 input / 32,768 output tokens; Thinking levels minimal/medium/high; Google Web and Image Search grounding; up to 14 reference images; 1K/2K/4K output (default 1K); Batch API supported; caching not supported. DeepMind model card (Published 6 October 2026) says it is based on Gemini 3.6 Flash.